Non-Invasive Monitoring of Cerebral Edema Using Ultrasonic Echo Signal Features and Machine Learning.

Area of Science:

• Biomedical Engineering
• Neuroscience
• Medical Imaging

Background:

• Cerebral edema is a critical complication of brain injury, leading to high mortality and disability.
• Accurate and timely diagnosis of cerebral edema is essential for effective patient management.
• Current monitoring methods often lack real-time capabilities or are invasive, highlighting the need for advanced techniques.

Purpose of the Study:

• To develop and validate a non-invasive method for real-time monitoring of cerebral edema.
• To classify different types of cerebral edema using ultrasound technology.
• To predict the cerebral infarction volume ratio in a rodent model.

Main Methods:

• Acute cerebral edema was induced in rats via middle cerebral artery occlusion.
• Ultrasonic echo signals were analyzed in time and frequency domains over 24 hours.
• Machine learning algorithms, including Random Forest, were employed for classification and prediction tasks.

Main Results:

• The Random Forest model, integrating 16 ultrasonic features, achieved 97.9% accuracy in classifying cerebral edema types.
• The model accurately predicted the cerebral infarction volume ratio with an R² value of 0.8814.
• Histomorphological changes were examined concurrently with ultrasound data.

Conclusions:

• The proposed ultrasound and machine learning strategy effectively classifies cerebral edema and predicts infarct volume.
• This non-invasive approach shows significant promise for real-time cerebral edema monitoring in clinical settings.
• Fusion of ultrasound features with machine learning offers a novel diagnostic tool for brain injury assessment.